The UT^2 bot, which had a
humanness rating of 27.2727% in BotPrize 2010,
is based on two core ideas:
(1) multiobjective neuroevolution is used
to learn skilled combat behavior, but filters on
the available combat actions ensure that the
behavior is still human-like despite being evolved
for performance, and (2) a database of traces of
human play is used to help the bot get unstuck
when its navigation capabilities fail.
Several changes have recently been made to UT^2:
Extra input features have been provided to the
bot to help it evolve better combat behavior, the
role of human traces in the navigation of the bot has
been expanded, and an extra control module has been added
which encourages the bot to observe other
players the way a human would, rather than simply
battle them. These changes should make UT^2
act more human-like in this year's BotPrize competition.